Annotation

Annotation: In the modern world, the banking sector faces a number of challenges related to risk management in project investments. These risks can be of different nature, including financial, operational, credit and market. With the development of technology and the introduction of artificial intelligence in various areas of business, the emphasis on the use of AI methods in risk management in the banking sector is becoming increasingly significant. Artificial intelligence has the potential to significantly improve the processes of data analysis, forecasting and decision making, which, in turn, can improve the efficiency of risk management. However, the introduction of AI methods also leads to the emergence of new problems and challenges. One of the key issues is the need for high-quality data for training AI models, as well as overcoming issues related to ethical aspects and transparency of algorithms. The specifics of banking activities require not only high accuracy of algorithms, but also compliance with strict regulatory standards, which adds complexity to the process of integrating AI methods. This article discusses the issue of using artificial intelligence in project management risks in a bank. The article analyzes AI methods such as machine learning and big data analytics, their use in credit risk assessment, preliminary research and monitoring of various stages of the project cycle. Particular attention is paid to how these technologies can help in identifying potential threats and responding to them as quickly as possible. The purpose of the article is to reveal the advantages and disadvantages of approaches to applying artificial intelligence in risk management in IT projects of banks. We will try to identify in which cases the use of AI methods can significantly improve risk management processes, and in which cases it can entail additional difficulties and risks. The study will present examples of successful application of AI methods in the banking sector, as well as substantiate the key factors contributing to the successful integration of technologies. This will allow a deeper understanding of the difficulties and benefits associated with the implementation of artificial intelligence in risk management, which is an important aspect for modern banking policy and strategy.